Exploring State-of-the-art models for Early Detection of Forest Fires
This work addresses early warning for forest fires, which is critical for environmental protection and safety, but it is incremental as it builds on existing methods with a new dataset.
The paper tackled the problem of early forest fire detection by creating a synthetic dataset from game simulators and combining it with existing images to address data scarcity, and they compared YOLOv7 and detection transformer models on this dataset, achieving competitive results but without specific numerical gains reported.
There have been many recent developments in the use of Deep Learning Neural Networks for fire detection. In this paper, we explore an early warning system for detection of forest fires. Due to the lack of sizeable datasets and models tuned for this task, existing methods suffer from missed detection. In this work, we first propose a dataset for early identification of forest fires through visual analysis. Unlike existing image corpuses that contain images of wide-spread fire, our dataset consists of multiple instances of smoke plumes and fire that indicates the initiation of fire. We obtained this dataset synthetically by utilising game simulators such as Red Dead Redemption 2. We also combined our dataset with already published images to obtain a more comprehensive set. Finally, we compared image classification and localisation methods on the proposed dataset. More specifically we used YOLOv7 (You Only Look Once) and different models of detection transformer.